LEADER 05589nam 22007815 450 001 996465540803316 005 20210216211524.0 010 $a3-540-45428-4 024 7 $a10.1007/3-540-45428-4 035 $a(CKB)1000000000211768 035 $a(SSID)ssj0000325035 035 $a(PQKBManifestationID)11211312 035 $a(PQKBTitleCode)TC0000325035 035 $a(PQKBWorkID)10321026 035 $a(PQKB)10821859 035 $a(DE-He213)978-3-540-45428-1 035 $a(MiAaPQ)EBC3073174 035 $a(PPN)155208500 035 $a(EXLCZ)991000000000211768 100 $a20121227d2002 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMultiple Classifier Systems$b[electronic resource] $eThird International Workshop, MCS 2002, Cagliari, Italy, June 24-26, 2002. Proceedings /$fedited by Fabio Roli, Josef Kittler 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (X, 342 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v2364 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-43818-1 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aInvited Papers -- Multiclassifier Systems: Back to the Future -- Support Vector Machines, Kernel Logistic Regression and Boosting -- Multiple Classification Systems in the Context of Feature Extraction and Selection -- Bagging and Boosting -- Boosted Tree Ensembles for Solving Multiclass Problems -- Distributed Pasting of Small Votes -- Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy -- Highlighting Hard Patterns via AdaBoost Weights Evolution -- Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse -- Ensemble Learning and Neural Networks -- Multistage Neural Network Ensembles -- Forward and Backward Selection in Regression Hybrid Network -- Types of Multinet System -- Discriminant Analysis and Factorial Multiple Splits in Recursive Partitioning for Data Mining -- Design Methodologies -- New Measure of Classifier Dependency in Multiple Classifier Systems -- A Discussion on the Classifier Projection Space for Classifier Combining -- On the General Application of the Tomographic Classifier Fusion Methodology -- Post-processing of Classifier Outputs in Multiple Classifier Systems -- Combination Strategies -- Trainable Multiple Classifier Schemes for Handwritten Character Recognition -- Generating Classifier Ensembles from Multiple Prototypes and Its Application to Handwriting Recognition -- Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data -- Stacking with Multi-response Model Trees -- On Combining One-Class Classifiers for Image Database Retrieval -- Analysis and Performance Evaluation -- Bias?Variance Analysis and Ensembles of SVM -- An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs -- Reduction of the Boasting Bias of Linear Experts -- Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers -- Applications -- Boosting and Classification of Electronic Nose Data -- Content-Based Classification of Digital Photos -- Classifier Combination for In Vivo Magnetic Resonance Spectra of Brain Tumours -- Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach -- A Multi-expert System for Movie Segmentation -- Decision Level Fusion of Intramodal Personal Identity Verification Experts -- An Experimental Comparison of Classifier Fusion Rules for Multimodal Personal Identity Verification Systems. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v2364 606 $aEnsemble learning (Machine learning) 606 $aComputer engineering 606 $aArtificial intelligence 606 $aPattern recognition 606 $aOptical data processing 606 $aAlgorithms 606 $aComputer Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/I27000 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aEnsemble learning (Machine learning) 615 0$aComputer engineering. 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 0$aOptical data processing. 615 0$aAlgorithms. 615 14$aComputer Engineering. 615 24$aArtificial Intelligence. 615 24$aPattern Recognition. 615 24$aImage Processing and Computer Vision. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a006.3/1 702 $aRoli$b Fabio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKittler$b Josef$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Workshop on Multiple Classifier Systems 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465540803316 996 $aMultiple Classifier Systems$9772217 997 $aUNISA